How Generative AI Redefines Personalization and Diversity in the Newsroom
Cheng Yu, Jim Chi, Aaron J. Snoswell, Orestis Papakyriakopoulos
The tension between news personalization and information diversity has traditionally been understood as a matter of different content reaching different people, with diversity evaluated by the range of sources, topics, and perspectives to which audiences are exposed. Generative AI outdates this understanding, since it transforms personalization in two ways: how journalists adapt news to audiences and how the AI tools journalists use personalize content production to their own preferences. Drawing on 16 semi-structured interviews with media professionals across public service, commercial, and state-affiliated news settings, we examine how these practices reshape editorial judgment and the conditions under which news diversity is produced. We find that participants negotiate AI integration by maintaining authority over meaning while delegating aspects of content presentation. Yet the tools they adapt for their own work also mediate which sources and perspectives become available, introducing diversity-relevant decisions that remain largely invisible to organizational oversight. While participants anticipate diversity risks in audience-facing personalization, they predominantly interpret diversity-imposed limitations in their own tools as technical problems. As a result, decisions that shape news diversity increasingly occur before publication and across multiple versions of a story, where neither evaluation of published items nor human approval can readily observe them. Journalists thus remain formally responsible for outcomes shaped by decisions they cannot fully see or control. Our findings highlight that safeguarding news diversity requires looking beyond published content to the professional practices, infrastructures, and distributions of control that shape it.
尚无 AI 解读，展示原文摘要